{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_create\n",
    "def create_date():\n",
    "    iris = load_iris()\n",
    "    df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "    df['label'] = iris.target  \n",
    "    df['test']='0'\n",
    "    df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label','test']\n",
    "    data = np.array(df.iloc[:100, [0,1,-2]])\n",
    "    return data[:,:2],data[:,-1]\n",
    "X,y=create_date()\n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[5.5, 4.2],\n",
       "        [4.4, 3. ],\n",
       "        [4.6, 3.4],\n",
       "        [6.1, 2.8],\n",
       "        [5.7, 3. ]]),\n",
       " array([0., 0., 1., 1., 0.]))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)\n",
    "X_train[:5],y_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "class logisticRegressionClassifier:\n",
    "    def __init__(self,max_iter=200,learning_rate=0.01):\n",
    "        self.max_iter=max_iter\n",
    "        self.learning_rate=learning_rate\n",
    "\n",
    "    def sigmoid(self,x):\n",
    "        return 1/(1+np.exp(-x))\n",
    "\n",
    "    def fit (self,X,y):\n",
    "        X = np.hstack((np.ones((X.shape[0],1)),X))\n",
    "        self.weight= np.zeros((X.shape[1],1))  \n",
    "        y = np.expand_dims(y,axis =1)\n",
    "        # training\n",
    "        for iter_ in range(self.max_iter):\n",
    "            h = self.sigmoid(np.dot(X,self.weight))\n",
    "            error = y-h\n",
    "            self.weight = self.weight+self.learning_rate * np.dot(X.T, error)\n",
    "    def predict(self,x):\n",
    "        x = np.hstack((np.ones(x.shape[0],1),x))\n",
    "        pred = self.sigmoid(np.dot(x, self.weight))\n",
    "        if pred>0.5:\n",
    "            return 1\n",
    "        else:\n",
    "            return 0\n",
    "        \n",
    "    def score(self,X_test,y_test):\n",
    "        right = 0\n",
    "        X_test = np.hstack((np.ones((X_test.shape[0],1)),  X_test))\n",
    "        res = np.dot(X_test , self.weight)\n",
    "        for (result,y) in zip(res,y_test):\n",
    "            if (result>0 and y==1) or (result < 0 and y == 0):\n",
    "                right+=1\n",
    "        return right / len(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "instance =logisticRegressionClassifier()\n",
    "instance.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.12394099],\n",
       "       [ 4.41373639],\n",
       "       [-7.34073206]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f304aee9810>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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azsrnl/e0Z6fvyTvLXS/u5tpV29iZU8Ij85LZ9thcfvydiySYO5Shm6JKqSBgD5AEPKu13tVCsxlKqU+BIuBHWuvPWuhnGbAMIC4urtODFvbhaeUejP9xo+2ckHvvdDtzSkhNy2R7VgkX9A1hxfzR3DFjJOGhUnjZ6QwFdK21B5iolIoA3lJKjdNaH2rWZC8w0rcsswDYCCS30M9aYC14b4p2cezCBoKUajFYB/k9hMloOyfk3juR1pptWadJ3ZTF7qNnGNy/D/+2YAzfmx5HPym87BodykPXWpcC6cB3/I6XNy3LaK3fA0KUUoNNGqOwsSXTYw0dN9rOCbn3TqK1Ju2Lk9yw+mNuX7ebY2eq+MW1F7PtsbncMytBgrnLGMlyGQLUa61LlVJhwFXA//i1GQ6c1FprpdQ0vH9RlARiwMJemm5otpe9YrSdE3LvnaCxUfPPz0+SmpbJocJyoiPC+OUN47hpcgx9gqVWp1u1m4eulJoAvAwE4Q3Ur2utn1RK3QegtV6jlFoO3A80ANXAo1rrj9vqV/LQhTCfx1d4+dnNWXxxooL4yL48MDeJGyZFEyL1Ol2hrTx02VgkhAs0eBr526dFPLs5i+zicyQN7c/yuUksnDBCCi+7jBS4cLBAbHQxusHH7P6sKphh9nztpK6hkbf2FbA6PZu8kiouGh7Os9+7lKv1VnqlPQhvF8DAGJj3M5hwi9XDFQEmAd3GArHRxegGH7P7s6pghtnztYvaBg+vZxSwJj2bwtJqxkcPZO3tk7lqzDB6HfoLvPsw1Psydsry4Z2HvT9LUHc1+beYjQVio4vRDT5m92d0LmbP2ez5Wq26zsMft+Uy61eb+feNhxg2oA8v3jWVvy2fybfHDqdXLwWbnvw6mDepr/YeF64mV+g2FoiNLkY3+Jjdn1UFM8yer1XO1Tbwys48/rA1h9OVdUwfNYjf3TKRyxMjv1l4uayg5U5aOy5cQwK6jQVio4vRDT5m92dVwQyz59vdymvqWf/xUdZty+VsVT1XJg/moZRkpo1qo17nwBjvMktLx4WryZKLjQVio4vRDT5m92dVwQyz59tdSqvq+N0/v2TmU2n85h9fMinuAt564HL+9P3pbQdz8N4ADfH7CzAkzHtcuJpcodtYIDa6GN3gY3Z/VhXMMHu+gXa6spZ123JZ//FRztV5mD92GA+lJDMueqDxTppufG560rvMIlkuPYbkoQthA6fKa3h+Sw6v7sqjtqGRa8aPYHlKEhcNl8LL4nyShy4Cyqr8cjcoKq1mzUfZvPZJPp5GzXWXRPHA3CSShva3emidd+B1+deBRSSgiy6xKr/c6fLPVLE6PYs39hSgNdw0OYb75yQyMrKf1UPrmgOve3PeJQfeEhLQRZe0lTfePFAbbed2OcWVPLs5m437CwlSisVT47hvTiLRLnhEL9B2DrwE9ICTgC66xKr8cqf58mQFq9KyePdAEb2De/GvM+K5d3YCwwa4rF6n5MBbSgK66BKr8sud4rOiMlalZfH+oRP07R3EPbMSWHpFAkPCXVriTXLgLSV56KJLrMovt7tP80tZ+vInXLNyG9syT/NQShLbH0vh8avHuDeYg+TAW0yu0EWXWJVfblcZR8+wMi2LLV8WMzAshEe/dSH/enk8A8N6SL1OyYG3lOShC9FFWmt25JSQuimLHTklRPbrzdIrE7h9xkj6S4k3YTLJQ+9GZudaG+3Pymd+99T8cq01WzJPk7opk4y8swwN78MT13gLL/ftHaD/tdyU4+2muRgV4DlLQDeR2bnWRvuz8pnfPTG/XGvNps9PkZqWyacFZUQNDOXJ68Zyy5RYQkMCWK/TTTnebpqLUd0wZ7kpaiKzn+VttD8rn/kdiGe221Wjr17ngpXbWLo+gzNVdfz3jeNJXzGXO2bEBzaYg7uec+6muRjVDXOWK3QTmZ1rbbQ/K5/53RPyyz2NmncPFLEqLYvMU5UkDO7Hb26+hOsmRnVv4WU35Xi7aS5GdcOcJaCbyOxca6P9WfnMbzfnl9d7Gtm4r5DV6dnknj7HhcP688ziiSycEEVQLwuep+6mHG83zcWobpizLLmYyOxca6P9WfnMbzfml9c1NPLnXcdI+W06K944QFhIEM/deikfPDKL6yZGWxPMwV053m6ai1HdMGe5QjeR2bnWRvuz8pnfbsovr6n38L+f5LPmo2yOl9VwSWwEv7h2LCkXDf1mmTcruCnH201zMaob5ix56KLHq6pr4M+7jvH8lhyKK2qZGn8BD6Ukc2XyYHsEciGakTx0BwtEjrcTctu7Q2VtA+t3HGXd1lxKztUxIyGSlYsncVnCIAnkbvbuo7DnJdAeUEEw+U5Y+Luu9WmTnHoJ6DYWiBxvJ+S2B1pZdT0vbT/KH7fnUlZdz+wLh/BQShJT4tup1Smc791HIWPd16+15+vXnQ3qNsqpl5uiNhaIHG8n5LYHyplzdfzmwyNc8VQav/+/L5kaP4i3H5zJy3dPk2DeU+x5qWPHjbBRTr1codtYIHK8nZDbbrbiilpe2JrDn3bmUV3v4epxw3lwbhJjozpQeFm4g/Z07LgRNsqpl4BuY4HI8XZCbrtZTpTV8PyWbDbsPkZdQyPXXhLF8rlJJA8Lt3powioqqOXgrbqwy9dGOfWy5GJjgcjxdkJue1cVnK3iiY0HmfWrzazfkcfCCVH836OzeWbxJAnmPd3kOzt23Agb5dTLFbqNBSLH2wm57Z119PQ5nkvP5q97C1AKbpocywNzEokd1NfqoQm7aLrxaWaWi41y6iUPXThe1qlKnt2cxdv7CwkO6sWSqbHcOzvRFY8fEMJfl/LQlVKhwBagj6/9G1rrn/u1UcAzwAKgCrhTa723qwMXoi1fnCgnNS2L9w4eJzQ4iLtnjmLZrASGuq3wshAGGVlyqQVStNaVSqkQYJtS6n2t9c5mba4Gkn2/pgPP+X53DaObcZxQ7MHohiG7zvlQYRkrN2Xyj8Mn6dc7iPtmJ7L0ilFE9m+lVmcgNn0Y3Zxi9mfbvT8rGZ2Lm+bsp92Arr1rMpW+lyG+X/7rNNcB631tdyqlIpRSI7TWx00drUWMbsZxQrEHoxuG7DjnvcfOkropk81HigkPDebhecncPTOeiL69W/9Dgdj0YXRzitmfbff+rGR0Lm6acwsMZbkopYKUUvuBU8A/tda7/JpEA83zdgp8x1zB6GYcJxR7MLphyE5z3pVTwm0v7OLG1R+zP7+UH337Qrb/JIVHv3Vh28EcArPpw+jmFLM/2+79WcnoXNw05xYYynLRWnuAiUqpCOAtpdQ4rfWhZk1aSk7+xt1WpdQyYBlAXFxcx0drEaObcZxQ7MHohiGr56y1ZntWCSvTMtmde4bB/fvw0wUXcev0kfTrSOHlQGz6MLo5xezPtnt/VjI6FzfNuQUdykPXWpcC6cB3/N4qAJonKMcARS38+bVa6yla6ylDhgzp2Egt1Fq2hP9xo+2s1NrGIP/jVs1Za83mL05x43Mfc9u6XeSVnOPn117MtsfmsmxWYseCObS+uaMrmz5a24Tif9zsz7Z7f1YyOhc3zbkF7QZ0pdQQ35U5Sqkw4CrgC79mfwPuUF6XAWVuWT8H45txnFDsweiGoe6ec2Oj5sPPTrBo1XbueukTTpXX8l/Xj2PLj+dy18xRna/XGYhNH0Y3p5j92Xbvz0pG5+KmObfAyOXOCOBlpVQQ3r8AXtdav6uUug9Aa70GeA9vymIW3rTFuwI0XksY3YzjhGIPRjcMddecPY2a9w8dZ1VaFl+cqGBkZF9+9d0J3HBptDn1OgOx6cPo5hSzP9vu/VnJ6FzcNOcWyMYiYYkGTyPv+AovZxefI3FIP5anJHHthCiCu7PwshAOIwUuupET8tCtVNfgLbz8bHoWeSVVXDQ8nFXfm8TV40ZYV6uzM+ye8xyI8dk9B97F+eVGSUA3kRPy0K1S2+Dh9YwC1qRnU1hazbjoATx/+2S+NWYYvZwUyMH+Oc+BGJ/dc+Bdnl9ulCy5mGjmU2ktPpo2OiKM7T9JsWBE1quu87Bh9zGe35LNyfJaJsVF8HBKMnNGD3Fumbffj2vlcamx8P8OdbydE8Zn9lzs3p+NyZJLN3FCHnp3OVfbwCs78/jD1hxOV9YxbdQgfnvzRGYmRTo3kDexe85zIMZn9xx4l+eXGyUB3USBKEjhNOU19az/+CjrtuVytqqeK5IG81BKEtMTIq0emnmMFjSwqvBBIMZn9lzs3p9DSTqBiZyQhx4opVV1/O6fX3LFU2n85h9fMjE2gjcfuJxXlk53VzAH++c8B2J8ds+Bd3l+uVFyhW4iJ+Shm62kspYXtuXypx15VNY2MH/sMJbPTWZ8jIvrddo95zkQ47N7DrzL88uNkpuiolNOldewdksOr+46Rk2DhwXjR7B8bhJjRgywemhCuJrcFBWmKSqt5vmPstnwST4NnkaumxjNg3MTSRoqtTpbZPS56Vax+/jA/rn8NiIBXRiSf6aK1enZvLEnH63hu5fGcP+cROIH97N6aPZl9LnpVrH7+MD+ufw2I0suok25p8/x7OYs3tpXSJBS3DI1hvtmJxJzgRRebtd/DGr5UbsqCH5+pvvH48/u4wP75/JbQJZcRIdlnqxg1eYs3vm0iJCgXtwxYyT3zkpk+ECp12mY0eemW8Xu4wP75/LbjAR0cZ7DReWs2pzJ+4dOEBYSxD1XJrD0ygSGhLdSr1O0TgW1fgVsB3YfH9g/l99mJA9dAPBpfilLX85gwcqtbP3yNA/OSWLbYyk8vmCMBPPOMvrcdKvYfXxg/1x+m5Er9B4u4+gZVqZlseXLYgaGhfD/rrqQO2fGMzAsxOqhOZ/R56Zbxe7jA/vn8tuM3BTtgbTW7Mw5w8pNmezIKWFQv94svXIUt182kvBQCeRC2JncFBWAN5BvyTzNqrRMPjl6liHhfXjimjF8b3ocfXvb6D8FJ+T/mj1Gs/PBnfAdCtPZ6P9iEShaazZ9forUzVl8ml/KiIG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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_points = np.arange(4,8)\n",
    "y_ = -(instance.weight[1]*x_points + instance.weight[0])/instance.weight[2]\n",
    "plt.plot(x_points,y_)\n",
    "plt.scatter(X[:50,0],X[:50,1],label = '0')\n",
    "plt.scatter(X[50:,0],X[50:,1],label = '1')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=200)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "quick = LogisticRegression(max_iter=200)\n",
    "quick.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2.7544687 ]\n",
      " [-2.56650858]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-6.91203611])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quick.score(X_test,y_test)\n",
    "print(quick.coef_.T)\n",
    "quick.intercept_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f304aef9cd0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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   "source": [
    "x_ponits = np.arange(4, 8)\n",
    "y_ = -(quick.coef_[0][0]*x_ponits + quick.intercept_)/quick.coef_[0][1]\n",
    "plt.plot(x_ponits, y_)\n",
    "\n",
    "plt.plot(X[:50, 0], X[:50, 1], 'o', color='blue', label='0')\n",
    "plt.plot(X[50:, 0], X[50:, 1], 'o', color='orange', label='1')\n",
    "plt.xlabel('sepal length')\n",
    "plt.ylabel('sepal width')\n",
    "plt.legend()"
   ]
  }
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